AI in SEO

Everyone's freaking out about GEO, LLMO, and AEO. After 7 months of running tests across tons of sites… I can tell you this: It's all built on SEO fundamentals. The same principles that rank you on Google also get you cited in ChatGPT, Claude, and Perplexity. So before you buy into shiny new tactics that promise “AI visibility”…here's what actually moves the needle: 1. Trust Signals AI tools pull from review platforms to assess business credibility and expertise. Build trust signals in the right places: - Local businesses: prioritize Google Business Profile reviews and responses - SaaS companies: maintain strong G2 and Capterra profiles  - Ecommerce: focus on Trustpilot or industry-specific review platforms - Respond to reviews professionally and keep profiles updated 2. Document Structure LLMs love well-structured documents. Instead of optimizing just for human readers, structure content for AI platforms too: - Add company context throughout documents. Instead of "our latest update," write "Acme Corp's Q4 2024 update" - Use clear headings and comprehensive sections that can stand alone - Include key facts in multiple formats (inline text, bulleted lists, data tables) 3. Link Building for Relevance Quality and topical relevance matter more than quantity for AI visibility. Focus your link building efforts: - Target industry-relevant sites where your brand mention makes logical sense - Pursue guest posts and collaborations within your industry - Don't ignore nofollow links from high-authority sites in your niche - Seek brand mentions even without direct links. (the mention itself carries weight) Avoid completely unrelated sites. 4. Topical Authority Still Rules LLMs are trained on the same web content that Google indexes. The more deep, high-quality content you publish around your niche, the more AI systems recognize you as the go-to source, the more you get mentioned. Take out the trash. Delete random blog posts about topics unrelated to your business. They're actually hurting your AI visibility. 5. Be everywhere LLMs crawl Repurpose your content across Reddit, Medium, LinkedIn, and YouTube. These platforms get crawled heavily by AI, and showing up on them regularly builds brand visibility. LLMs love patterns. The more places they see you, the more they assume you’re an authority. 6. Technical setup - Use HTML-driven pages - Add schema markup - Clean site architecture (no page more than 3 clicks from homepage) - Ensure your critical content loads server-side (most AI crawlers don't render JavaScript) 7. Traditional Search Feeds AI Most AI tools use Bing or Google's index for real-time data. Better search rankings directly improve AI visibility.

Website traffic was a valuable metric correlated to growth. Now it may be a vanity metric, not correlated to growth. Search has been disrupted. Visits to your website are declining. So, marketers - what now? The search landscape was already shifting (I talked about this at INBOUND last year). Now, the change is accelerating dramatically: - AI Overviews appear in 43% of Google searches – when they do, organic CTR drops by nearly 35%. - Google’s AI Mode and audio AI overviews are coming – they will cause clicks to collapse further. - More buyers are using LLMs to find information, ChatGPT search in Europe grew 3.7x in six months. So, what should marketers do? And how can AI help? 1. Be everywhere and diversify your channels The days of relying solely on Google search are way over. You need to show up on YouTube, LinkedIn, Instagram, podcasts, and in niche communities. The good news? AI makes multi-channel, multi-format content creation scalable – even for small teams. 2. Be specific with context In the past, broad informational content was the way to rank in Google. Today, buyers expect results deeply relevant to them, whether they’re on Google, LLMs, or Reddit. You need specific content that reflects your expertise and resonates with your buyers. 3. Optimize for conversion, not clicks Traffic was once the lever you could pull. Now, conversion is where the opportunity lies. AI enables you to deliver personal messages that drive better conversion. Don’t ask, “How do we get more blog visits?” Ask, “How do we convert more prospects into customers across all channels?” The changes in search are sending shockwaves across marketing teams and media companies everywhere. The era of traffic-based marketing is ending. But a new era full of opportunity is just beginning. Super exciting times for marketers to reinvent the playbook!

WTH is a vector database and how does it work? If you’re stepping into the world of AI engineering, this is one of the first systems you need to deeply understand 👇 🧩 Why traditional databases fall short for GenAI Traditional databases (like PostgreSQL or MySQL) were built for structured, scalar data: → Numbers, strings, timestamps → Organized in rows and columns → Optimized for transactions and exact lookups using SQL They work great for business logic and operational systems. But when it comes to unstructured data, like natural language, code, images, or audio- they struggle. These databases can’t search for meaning or handle high-dimensional semantic queries. 🔢 What are vector databases? Vector databases are designed for storing and querying embeddings: high-dimensional numerical representations generated by models. Instead of asking, “Is this field equal to X?”- you’re asking, “What’s semantically similar to this example?” They’re essential for powering: → Semantic search → Retrieval-Augmented Generation (RAG) → Recommendation engines → Agent memory and long-term context → Multi-modal reasoning (text, image, audio, video) ♟️How vector databases actually work → Embedding: Raw input (text/image/code) is passed through a model to get a vector (e.g., 1536-dimensional float array) → Indexing: Vectors are organized using Approximate Nearest Neighbor (ANN) algorithms like HNSW, IVF, or PQ → Querying: A new input is embedded, and the system finds the closest vectors based on similarity metrics (cosine, dot product, L2) This allows fast and scalable semantic retrieval across millions or billions of entries. 🛠️ Where to get started Purpose-built tools: → Pinecone, Weaviate, Milvus, Qdrant, Chroma Embedded options: → pgvector for PostgreSQL → MongoDB Atlas Vector Search → OpenSearch, Elasticsearch (vector-native support) Most modern stacks combine vector search with keyword filtering and metadata, a hybrid retrieval approach that balances speed, accuracy, and relevance. 🤔Do you really need one? It depends on your use case: → For small-scale projects, pgvector inside your Postgres DB is often enough → For high-scale, real-time systems or multi-modal data, dedicated vector DBs offer better indexing, throughput, and scaling → Your real goal should be building smart retrieval pipelines, not just storing vectors 📈📉 Rise & Fall of Vector DBs Back in 2023–2024, vector databases were everywhere. But in 2025, they’ve matured into quiet infrastructure, no longer the star of the show, but still powering many GenAI applications behind the scenes. The real focus now is: → Building smarter retrieval systems → Combining vector + keyword + filter search → Using re-ranking and hybrid logic for precision 〰️〰️〰️〰️ ♻️ Share this with your network 🔔 Follow me (Aishwarya Srinivasan) for data & AI insights, and subscribe to my Substack to find more in-depth blogs and weekly updates in AI: https://lnkd.in/dpBNr6Jg

Nobody’s clicking anymore. And it's not because they're bouncing. They’re finishing their journey before they ever reach you. Pew analyzed browsing behavior from March 2025 and found: when an AI Overview appears, people click a traditional search result 8% of the time (vs 15% without an AI Overview). And they’re more likely to end the session right there (26% vs 16%).  Then Seer Interactive took a different cut: by Sept 2025, the organic click-through rate on queries with AI Overviews was 0.61%, down from 1.76% in June 2024 — a 61% decline. So what are users doing? • They read the AI Overview then stop. • They search again instead of leaving Google. • They default to Wikipedia, YouTube or Reddit. • They talk it up with their LLM chatbot of choice. • ...or they just scroll social media instead. When I first introduced the concept of Zero-Click Content in 2022, I was talking about platforms rewarding in-app behavior. I didn't realize how fast we'd go from optimizing for impressions to optimizing to *be* the answer. Now we’re already living the next era of Zero-Click Marketing: win attention where your audience already is, and earn the right to be cited, remembered, and searched for by name. So what are you optimizing for in 2026? Clicks, citations, or recall?

If you search for "How to lower my bill" in a standard SQL database, you might get zero results if the document is titled "AWS Cost Optimization Guide." Why? Because the keywords don't match. This is the fundamental problem Vector Databases solve. They allow computers to understand that "lowering bills" and "cost optimization" are semantically identical, even if they share no common words. Here is the end-to-end flow of how we move from Raw Data to Semantic Search (as illustrated in the sketch): 1. The Transformation (Vectorization) Everything starts with Embeddings. We take raw text, images, or code and pass them through an Embedding Model (like OpenAI or Cohere). Input: "Reduce AWS cloud costs" Output: [0.12, -0.83, 0.44...] We turn meaning into numbers. 2. The Heart (Vector Store) We don't just store the text; we store the vector. Vector Index: Used for the semantic search (finding the "nearest neighbor" mathematically). Metadata Index: Used for filtering (e.g., "Only show docs from 2024"). 3. The Query Flow When a user asks, "How can I lower my AWS bill?" we don't scan for keywords. We convert the user's question into a vector. We look for other vectors in the database that are mathematically close to it. We retrieve the "AWS Cost Optimization Guide" because it is close in meaning, not just spelling. Why does this matter for GenAI? This is the backbone of RAG (Retrieval-Augmented Generation). LLMs can be confident but wrong (hallucinations). Vector DBs provide the "Relevant Context" (the ground truth) so the LLM can answer accurately based on your proprietary data. The future of search isn't about matching characters; it's about matching intent.

💡Vector databases have become one of the most important infrastructure layers in modern AI systems. Most of us use LLMs every day without realizing that vectors and similarity search are doing the heavy lifting underneath. Let’s find out why vector databases matter and how they power real world AI applications. 🔸What a Vector Database Really Is A storage and retrieval engine for high dimensional embeddings that allow models to search by meaning instead of keywords. 🔸Why AI Converts Everything to Vectors Embeddings capture semantic intent, structure, tone, and relationships between concepts in a way that machines can measure mathematically. This is what enables AI to interpret meaning the way humans do. 🔸How Vector Databases Work Embed → Index → Similarity Search → Rank → Reason. This pipeline is the foundation of retrieval augmented generation systems and intelligent search workloads. 🔸What Similarity Search Enables The engine can find items that are conceptually aligned even when they use different words or formats. This is semantic retrieval instead of lexical matching. 🔸Why Traditional Databases Fall Short Relational stores and document stores are optimized for structured data and exact match queries. They are not built for embeddings, cosine similarity computations, or efficient navigation of high dimensional spaces. 🔸Why Vector Databases Matter for AI They enable long term memory, reduce hallucinations, and create stable grounding for reasoning. This is critical when deploying LLMs in production use cases that require accuracy. 🔸How They Power RAG Systems Before a model generates an answer, the system pulls factual context from internal knowledge sources. This makes responses more reliable and aligned with a company’s domain knowledge. 🔸How Chatbots Use Them They maintain conversational context, retrieve business specific data, and interpret intent across multiple interactions. 🔸How Search Engines Benefit They support semantic, multimodal, and concept driven search that goes beyond simple keyword matching. 🔸Recommendations Powered by Vectors Embeddings map user behavior and item characteristics into a shared semantic space which allows for highly personalized and context aware recommendations. 🔸Popular Vector Databases in 2025 Pinecone, Weaviate, ChromaDB, FAISS, Milvus, Qdrant. 🔸Key Technical Features to Know Approximate nearest neighbor search, hybrid search with BM25 or dense retrieval, distributed indexing, sharded vector stores, real time embedding refresh, and LLM based re ranking. 🔹The Technical Reality Vector databases are now a foundational layer in the AI stack that enables multimodal understanding, agent memory, semantic reasoning, and enterprise grade reliability. I think that understanding how embedding architectures, similarity metrics, and vector stores work will give you a strong technical advantage as a developer. Save this doc for future reference. #VectorDatabases

Over the past two years, we’ve been fed a steady diet of “AI will change SEO.” And sure, it will — it already has. But that’s not the full picture. The AI shift that’s happening on Google’s homepage — through AI Overviews — isn’t just changing organic performance. It’s changing the entire search experience. Paid included. Let me spell it out: • Google’s AI Overviews are giving users full answers before they even think of clicking. • The format takes up prime real estate above the fold — pushing both paid and organic listings down. • In some cases, the overview takes the entire page. This isn’t a theoretical future. This is a visible, structural change to how results are served and seen, and as of a few days back it surpassed the 20% mark of all searhes. I pitched a major client this week who said, “AI isn’t a focus for us right now — we’re prioritizing Paid.” That’s the blind spot. Because whatever you’re running in Paid will be affected — through impressions, CTRs, conversions — all downstream from this new AI layer. If your strategy assumes that SEO is under attack while Paid stays safe… You're not seeing the whole board. Look below, you see paid ad?

My client showed up on Google’s AI Overview last week. Sounds great, right? Another win to post about. Another proof of strategy working. But here’s what most people miss: Showing up there isn’t luck. It’s not some overnight SEO hack. It’s not because we “sprinkled keywords like confetti.” It’s the byproduct of consistent, strategic work. Work rooted in understanding how search is evolving. Not just what Google wants. But what their buyers want. Here’s what the latest data actually reveals: → People Also Ask Shows up in 99.55% of AI Overview SERPs. If your content isn’t answering real buyer questions clearly and comprehensively? Good luck getting cited. → Related Searches Present on 99.04% of results. If your content doesn’t address connected queries with semantic depth? You’re missing half the game. → Organic Video On 89.85% of SERPs. Video isn’t just a nice brand add-on anymore. It’s prime SERP real estate. → Organic Site Links 67.10% inclusion. If your site structure and internal linking are a mess? You’re making it hard for Google to trust you. → Images and Carousels Appear in 33-40% of results. Original visuals and frameworks are winning attention. But here’s the real question: How did my client actually get there? Because it wasn’t by chance. It wasn’t by chasing AI trends. Here’s what we did instead: We mapped their ICP’s buying journey. We answered every core and adjacent question directly. We used structured data and intent matching to build clarity. We created walkthroughs, demos, and use-case explainers. Embedded them into their highest-value pages. We designed frameworks, process graphics, and original visuals. Not for decoration. For comprehension. We cleaned up their site architecture. Strengthened internal links. Ensured navigation felt effortless - for both search engines and humans. We prioritised recency and authority. Updated regularly. Backed every insight with expert quotes and customer stories. And we didn’t do it for AI. ✨ We did it for humans ✨ Because showing up in AI Overviews isn’t about chasing AI. It’s about being the best answer. The most trusted resource. The most human solution in an increasingly automated world. Solve real problems. Answer real questions. Create real value. Do that consistently - and AI will find you. Swipe to see the strategic framework we put into place with insights from Semrush. Need help? Drop me or Navneet Jha a message 👋 #contentmarketing #b2bsaas #aiseo #semrushambassador #seostrategy

For the first time in years, I’m seeing brands and agencies scramble not for keywords, but for citations in AI-driven feeds. Here’s what’s shifting (with some hard data + examples): 𝐀𝐈 𝐎𝐯𝐞𝐫𝐯𝐢𝐞𝐰𝐬 𝐚𝐫𝐞 𝐜𝐡𝐚𝐧𝐠𝐢𝐧𝐠 𝐒𝐄𝐑𝐏𝐬 • A study by BrightEdge found that 84% of Google searches now trigger an AI Overview in some capacity. • Click-through rates have dropped by up to 18–20% for brands that were previously ranking in the “blue links” but not included in the AI Overview. • Example: In searches for “best running shoes India,” Google’s AI snippet cites Nike, Adidas, Decathlon and a few review blogs but many strong e-commerce brands are completely missing. 𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦 𝐱 𝐆𝐨𝐨𝐠𝐥𝐞 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 • Recently, Instagram made posts indexable by Google. Early tests by SEO toolsets show that Reels and carousel posts are already appearing on page one for lifestyle searches (e.g., “best saree draping styles” or “Goa cafés to visit”). • This changes the game: social posts are no longer just engagement tools—they’re content assets for discoverability. • Brands like FabIndia and Zomato have already seen their Insta content rank on Google’s SERP in lifestyle + F&B categories. 𝐂𝐡𝐚𝐭𝐆𝐏𝐓 𝐌𝐞𝐧𝐭𝐢𝐨𝐧𝐬 𝐚𝐬 𝐭𝐡𝐞 𝐍𝐞𝐰 𝐏𝐑 • #ChatGPT and #Perplexity are fast becoming discovery engines in themselves. • In travel queries (“top luxury safari lodges India”), ChatGPT frequently mentions Taj Safaris, SUJÁN and Oberoi, which shows how brand mentions across the web now feed AI training and retrieval systems. • This is essentially PR + SEO converging into #AIO (AI Optimization). 𝐓𝐡𝐞 𝐁𝐢𝐠 𝐏𝐢𝐜𝐭𝐮𝐫𝐞 • By 2026, Gartner predicts that search traffic from AI assistants will rival traditional Google search. • Agencies can’t just be “SEO agencies” anymore - they need to advise clients on: - Structured data + schema for better AI parsing - PR placements that double up as AI citations - Social-first content designed for Google indexing My question to all agency leaders and SEO pros: 👉 Have you managed to crack the AIO playbook yet? What strategies (structured data, PR seeding, social optimization?) have worked for you to secure brand visibility inside AI feeds?

In an unsurprising move, Google is putting generative AI at the center of its most valuable real estate. The company is redesigning its homepage to feature “AI Overviews,” a mode that uses Gemini to synthesize information directly on the results page. For users, this means fewer blue links, more summarized answers, and the beginning of the transition from search engine to answer engine. The new feature, though not widely available yet, appears directly beneath the Google search bar beside the “Google Search” button, replacing the iconic “I’m Feeling Lucky” widget. But the real story isn’t the feature set. It’s the strategy. Channeling their inner Clayton Christensen, Google is embracing the Innovator’s Dilemma: disrupt yourself before someone else does. In this case, Google is cannibalizing its own search ad model (still the company’s financial backbone) to protect long-term dominance in AI. The trade-off is clear: less immediate ad revenue per query in exchange for deeper user engagement and a more defensible moat around the future of search. AI Overviews could drastically reduce traffic to websites, particularly publishers, retailers, and content creators who rely on Google referrals. That’s a known risk. However, the existential threat isn’t from content partners. It’s from OpenAI, Perplexity, and every startup aiming to turn AI into the next search interface. The transition from search engine to answer engine is going to be a rough one. But if I had to bet who will ultimately be “Google for AI Search”, I’m going with Google. -s